The present invention relates to methodologies for detecting tissue pathology, more particularly, to a method and apparatus for the early detection of tissue pathology using a wavelet decomposition method on tissue data obtained by using a multi-dimensional non-invasive imaging technique. Such method and apparatus provides a tool for physicians and researchers to diagnose and thus treat early stages of diseases or other disorders affecting suspect tissue to minimize irreversible tissue or other pathological damage caused by such disease.
(By xe2x80x9cpathologyxe2x80x9d I mean any abnormality or disorder of a tissue, muscle, organ, etc. In using the term xe2x80x9ctissuexe2x80x9d in the above paragraph and throughout this disclosure, xe2x80x9ctissuexe2x80x9d means tissue, muscle, organ, etc.)
Detection of diseased tissue by other than histological or biochemical means is a challenge for non-invasive imaging techniques. Pathologies of organ, muscle and tissue, such as various forms of cardiomyopathy, represent a group of diseases in which a non-invasive imaging technique to distinguish normal from abnormal tissue would be of particular importance. Texture analysis of organs, muscles or tissue, such as myocardium, is an approach to tissue characterization based on the spatial distribution of amplitude signals within a region-of-interest (ROI). While I use ultrasound and myocardium as the tissue of interest to describe how to make and use my invention, the invention can be used for images obtained by other multi-dimensional non-invasive imaging techniques, and to study other tissues, such as skeletal muscle, liver, pancreas, kidneys, and arterial wall linings. My invention is especially suitable when only a small ROI is available for analysis, such as a 16xc3x9716 ROI, as it has been stated by others that statistical methods are less reliable for small ROI""s as noise within the signal has a significant effect. However, my invention is also suitable for large ROI""S and, I speculate, is a better detector than statistical methods.
The characterization of myocardial tissue itself by ultrasound was attempted in 1957 where excised human hearts were used to distinguish infarcted from normal myocardium.
Other multi-dimensional non-invasive imaging techniques include magnetic resonance imaging (MRI), radionuclide imaging, and computer axial tomography (CAT Scan). Moreover, three-dimensional or higher order imaging may also be employed. However, in lieu of a two-dimensional wavelet transform, a three-dimensional wavelet should be substituted when a three-dimensional image is analyzed.
Methodologies to: characterize myocardium by ultrasound include quantitative estimates of frequency-dependent myocardial attenuation and backscatter (as referred to in Miller J G, Perez J E, Sobel B E, xe2x80x9cUltrasonic characterization of myocardium,xe2x80x9d Progress in Cardiovascular Diseases, copyright September/October 1985; XXVIII(2):85-110, all of wich is incorporated herein by reference) and have been subsequently used to distinguishnormal from abnormal myocardium (as referred to in Masuyama T, Valantine H R, Gibbons R, Schnittger I, Popp R L, xe2x80x9cSerial measurements of integrated ultrasonic backscatter in human cardiac allografts for the recognition of acute rejection,xe2x80x9d Circulation, copyright March 1990;81(3):829-839; Wickline S A, Thomas J L III, Miller J G, Sobel B E, Perez J E, xe2x80x9cSensitive detection of the effects of reperfusion on myocardium by ultrasonic tissue characterization with integrated backscatter,xe2x80x9d Circulation, copyright 1986;74:389-400; Sagar, K B, Rhyne T L, Pelc L R, Warltier D C, Wann L S, xe2x80x9cIntramyocardial variability in integrated backscatter: effects of coronary occlusion and reperfusion,xe2x80x9d Circulation, copyright 1987;75:436-442; and, Sagar K B, Pelc L R, Rhyne T L, Komorowski R A, Wann L S, Warltier D C, xe2x80x9cRole of ultrasonic tissue characterization to distinguish reversible from irreversible myocardial injury,xe2x80x9d JASE, copyright November/December 1990;3(6):471-477, all of which is incorporated herein by reference).
For the purposes of the present invention, the definition of image texture is xe2x80x9can attribute representing the spatial arrangement of the gray levels of the pixels in a regionxe2x80x9d (as referred to in xe2x80x9cIEEE Standard Glossary of Image Processing and Pattern Recognition Terminology,xe2x80x9d IEEE Press, copyright Mar. 26, 1990; 7.14, all of which is incorporated herein by reference). Tissue pathology which changes microscopic anatomical structure changes myocardial ultrasound texture (speckle), and echocardiographic texture does contain tissue structure related information (as referred to in Smith S W, Wagner R F, xe2x80x9cUltrasound speckle size and lesion signal to noise ratio: verification of theory,xe2x80x9d Ultras Imag, copyright 1984; 6:174; and, Wagner R F, Smith S F, Sandrick J M, Lopez H, xe2x80x9cStatistics of speckle in ultrasound B-scans,xe2x80x9d IEEE Trans Sonics Ultras, copyright May 1983; 30(3):186-163, all of which is incorporated herein by reference).
Attempting to numerically quantitate texture has been generally problematic with respect to the myocardium and, I speculate with respect to other organs, muscles and tissue. Quantization of texture using statistical techniques has been performed to identify various cardiomyopathic abnormalities, including myocardial contusion (as referred to in Skorton D J, Collins S M, Nichols J, Pandian N G, Bean J A, Kerber R E, xe2x80x9cQuantitative texture analysis in two-dimensional echocardiography: application to the diagnosis of experimental myocardial contusion,xe2x80x9d Circulation, copyright July 1983;68(1):217-223, all of which is incorporated herein by reference), amyloid infiltration (as referred to in Pinamonti B. Picano E, Ferdeghini E M, Lattanzi F, Slavich G, Landini L, Camerini F, Benassi A, Distante A, L""Abbate A, xe2x80x9cQuantitative texture analysis in two-dimensional echocardiography: application to the, diagnosis of myocardial amyloidosis,xe2x80x9d JACC, copyright September 1989; 14(3):666-671, all of which is incorporated herein by reference) hypertrophic cardiomyopathy (as referred to in Chandrasekaran K, Aylward P E, Fleagle S R, Burns T L, Seward J B, Tajik A J, Collins S M, Skorton D J, xe2x80x9cFeasibility of identifying amyloid and hypertrophic cardiomyopathy with the use of computerized quantitative texture analysis of clinical echocardiographic data,xe2x80x9d JACC, copyright Mar. 15, 1989;13(4):832-840, all of which is incorporated herein by reference), coronary ischemia (as referred to in Picano E, Faletra F, Marini C, Paterni M, Danzi G B, Lombardi M, Campolo L, Gigli G, Landini L, Pezzano A, Distante A, xe2x80x9cIncreased echodensity of transiently asynergic myocardium in humans: a novel echocardiographic sign of myocardial ischemia,xe2x80x9d JACC, copyrights January 1993;21(1):199-207, all of which is incorporated herein by reference), myocardial non-viability (as referred to in Marini C, Picano E, Varga A, Marzullo P, Pingitore A, Paterni M, xe2x80x9cCyclic variation in myocardial gray level as a marker of viability in man: a videodensitometric study,xe2x80x9d Eur Heart J, copyright March 1996;17:472-479, all of which is incorporated herein by reference), transplant rejection (as referred to in Stempfle H, Angermann C E, Kraml P, Schutz A, Kemkes B M, Theisen K, xe2x80x9cSerial changes during acute cardiac allograft rejection: quantitative ultrasound tissue analysis versus myocardial histologic findings,xe2x80x9d JACC, copyright July 1993;22(1)310-317, all of which is incorporated herein by reference) and myocarditis (as referred to in Ferdeghini E M, Pinamonti B, Picano E, Lattanzi F, Bussani R, Slavich G, Benassi A, Camerini F, Landini L, L""Abbate A, xe2x80x9cQuantitative texture analysis in echocardiography: application to the diagnosis of myocarditis,xe2x80x9d J Clin Ultrasound, copyright June 1991;19:263-270, all of which is incorporated herein by reference). These published reports used first- or second-order gray level histogram statistics for evaluation (usually 8-bit information), including mean gray level, standard deviation of the mean, skewness (deviation of the pixel distribution from a symmetrical shape), and the kurtosis (steepness of the pixel distribution).
The above-described statistical methods used for quantization of myocardial texture have limited capability. Data content for analysis was diminished in most of these studies because they were performed with digitized video signals for analysis. In addition, the ROI was relatively small (xcx9c16xc3x9716 pixel matrix) in order to avoid specular reflections (endocardial and epicardial borders). This small ROI limits the capability of statistical methodologies because of inherent noise in the image and a relatively small ROI to work with, making statistical methods less helpful.
I speculate that these identified problems with obtaining an image, and using statistical methods for relatively small ROI""s as are available, led to the difficulties in these becoming accepted methods for analysis, as such statistical methods do not work reliably.
One-dimensional wavelets are used in analysis of various time domain signals including evoked potentials (as referred to in Bertrand O, Bohorquez J, Pernier J, xe2x80x9cTime frequency digital filtering based on an invertible wavelet transform: An application of evoked potentials,xe2x80x9d IEEE Trans on Biomedical Engineering, copyright January 1994; 41(1):77-88, all of which is incorporated herein by reference), heart rate variability analysis (as referred to in Gamero L G, Risk M, Sobh J F, Ramirez A J, Saul J P, xe2x80x9cHeart rate variability analysis using wavelet transform,xe2x80x9d IEEE Computers in Cardiology, copyright 1996:177-180, all of which is incorporated herein by reference), and ventricular late potentials (as referred to in Batista A, English M, xe2x80x9cA multiresolution wavelet method for characterization of ventricular late potentials,xe2x80x9d IEEE Computers in Cardiology, copyright 1996:625-628; Meste O, Rix H, Caminal P, Thakor N V, xe2x80x9cVentricular late potentials characterization in time-frequency domain by means of a wavelet transform,xe2x80x9d IEEE Transactions on Biomedical Engineering, copyright July 994; 41(7):625-634, all of which is incorporated herein by reference). Two-dimensional wavelets are used for image compression (as referred to in Press W H, Teukolsky S A, Vetterling W T, Flannery B P, xe2x80x9cNumerical recipes in FORTRAN: The art of scientific computing,xe2x80x9d 2nd ed. New York: Cambridge University Press, copyright 1992:596-597; Chui C K, xe2x80x9cWavelets: A mathematical tool for signal analysis,xe2x80x9d Philadelphia: Society for Industrial and Applied Mathematics, copyright 1997: 178-180; Wickerhauser M V, xe2x80x9cAdapted wavelet analysis from theory to software,xe2x80x9d Wellesley, Massachusetts: A K Peters, copyright 1994:361-377, all of which is incorporated herein by reference), and also quantization of texture of various surfaces (as referred to in Prasad L, Lyengar S S, xe2x80x9cWavelet analysis with applications to image processing,xe2x80x9d Boca Raton: CRC Press, LLC, copyright 1997:235-239, 258-262, all of which is incorporated herein by reference), texture analysis in remote sensing (as referred to in Mecocci A, Gamba P, Marazzi A, Barni M, xe2x80x9cTexture segmentation in remote sensing images by means of packet wavelets and fuzzy clustering,xe2x80x9d International Society of Optical Engineering, Synthetic aperture radar and passive microwave sensing, copyright 1995:2584:142-151, all of which is incorporated herein by reference), and ultrasound texture analysis for characterization of fat content and marbling in beef cattle muscle (as referred to in Kim ND, Main V, Wilson D, Rouse G, Upda S, xe2x80x9cUltrasound image texture analysis for characterizing intramuscular fat content of live beef cattle,xe2x80x9d Ultrasonic imaging, copyright 1998; 20:191-205, all of which is incorporated herein by reference). Using the discrete wavelet transform (DWT), the approximation coefficients have been used for image compression, while detail coefficients of the wavelet deomposition correspond to texture information (as referred to in Prasad, L, Lyengar S S, xe2x80x9cWavelet analysis with applications to image processing,xe2x80x9d Boca Raton: CRC Press, copyright 1997:235-239, 258-262, all of which is incorporated herein by reference).
Recently, Mojsilovic et al. (as defined hereinafter) and Neskovic et al. (as defined hereinafter) used the two-dimensional Haar wavelet transform with an image extension method and wavelet decomposition to calculate texture energy, and differentiate viable from non-viable, myocardium (as referred to in Mojsilovic A, Popovic M V, Neskovic A N, Popovic A D, xe2x80x9cWavelet image extension for analysis and classification of infarcted myocardial tissue,xe2x80x9d IEEE Transactions on Biomedical Engineering, copyright September 1997; 44(9):856-866; and Neskovic A N, Mojsilovic A, Jovanovic T, Vasiljevic J, Popovic M, Marinkovic J, Bof ic M, Popovic A D. xe2x80x9cMyocardial tissue characterization after acute myocardial infarction with wavelet image decomposition: a novel approach for the detection of myocardial viability in the early post infarction period,xe2x80x9dCirculation, copyright Aug. 18, 1998; 98:634-641, all of which is incorporated herein by reference). However, the present invention differs from Neskovic et al. and Mojsilovic et al. in that my wavelet decomposition method and analysis does not use an image extension technique or a distance function. Moreover, unlike previous studies, the present invention utilizes a digitized xe2x80x9crawxe2x80x9d ultrasound image signal obtained immediately after the digital scan conversion. In other words, the present invention utilizes the digitized xe2x80x9crawxe2x80x9d ultrasound image signal from the digital scan converter which converts the polar coordinate format of the radio frequency ultrasound image signal to rectangular coordinates. Thus, such digitized xe2x80x9crawxe2x80x9d ultrasound image signal has not been subjected to image processing for video display. Such image processing techniques for smoothing or otherwise enhancing the images for video display are known in the trade.
Moreover, it should be further noted, that Neskovic et al., using the Haar wavelet transform with an image extension method and wavelet decomposition, found through thorough mathematical analysis that the energy of the vertical edge image (fLH) (a/k/a the horizontal detail coefficient) is the most reliable predictor of myocardial texture for the purpose of differentiating viable, myocardium from that of myocardial necrosis. Neskovic, et al. used a distance function to show these effects. More specifically, Neskovic et al. relied on the findings of Mojsilovic et al. whereby Mojsilovic et al. state that xe2x80x9cthe energy eLH is consistently the best feature for any level of decomposition. Since the measure eLH represents the energy distribution of vertical edges, this indicates that the changes in myocardial tissue structure are greatest in the horizontal direction, whereas, almost no difference can be observed in the vertical direction.xe2x80x9d
Furthermore, Neskovic et al. concluded that since an ultrasound image (echocardiogram) is by its very nature of relatively poor quality and that ultrasound noise is a random phenomenon essentially affecting the corner image (fHH) having the highest frequencies, Neskovic et al. argued that the corner image (fHH) having the highest frequencies should not be used.
In marked contrast to Neskovic et al., I have determined through further experimentation (described herein in detail below) that significant textural information can be derived from the corner image (fHH) (a/k/a the diagonal detail coefficient). In contrast to Neskovic et al. my preliminary animal studies suggest that the energy of the vertical edge image (fLH) (a/k/a the horizontal A detail coefficient) was the least sensitive of all the detail coefficients.
It appears, as opposed to Neskovic et al. and Mojsilovic et al., which found only the vertical edge (horizontal image) with a distance function (and not the texture measures directly), that direct textural energy from each set of detail coefficientsxe2x80x94vertical (v), horizontal (H), diagonal (D) and the sum of (H+V), and the sum of (H+V+D)xe2x80x94may be useful. Moreover, reperfusion was not determined by Neskovic et al. from the texture measures directly but by using a distance function.
I have discovered that, while the energy of the vertical edge image (fLH) (a/k/a the horizontal detail coefficient) approached significance in its ability to differentiate experimental groups (p≅0.07), the energy of the horizontal edge image (fHL) (a/k/a vertical detail coefficient); the energy of the corner image (fHH) (a/k/a the diagonal detail coefficient); the sum of the energies of the vertical detail coefficient and the horizontal detail coefficient; and, the sum of the energies of the vertical detail coefficient, the horizontal detail coefficient and the diagonal detail coefficient are significant in their ability to statistically discriminate groups (Table II).
As will be seen more fully below, the present invention is substantially different in methodology and approach from that of the prior detection methods for tissue image texture analyzing or characterization.
The preferred embodiment of the method and apparatus of the present invention solves the aforementioned problems in a straight forward and simple manner.
Broadly, what is provided is a method and apparatus for the early detection of tissue pathology using a wavelet decomposition method on tissue data obtained by using a non-invasive imaging technique. Such method and apparatus provide a tool for physicians and researchers to diagnose and thus treat early stages of diseases affecting suspect tissue to minimize irreversible tissue or other pathological damage caused by such disease.
More specifically, what is provided is a method and apparatus to detect early stages of cardiomyopathy using a wavelet decomposition method using a non-invasive imaging technique whereby such method and apparatus provides a tool for physicians and researchers to diagnose and thus treat early stages of cardiomyopathy affecting the myocardium to minimize irreversible myocardium damage.
Moreover, what is provided is a method and apparatus to detect early textural changes in an image of tissue or muscle using a wavelet decomposition method without the need for an image extension algorithm and/or distance function.
In view of the above an object of the present invention is to provide a tissue pathology detection apparatus which comprises a pathological tissue texture quantifier apparatus using wavelet decomposition to decompose an image of the tissue and a tissue pathology evaluator which compares a quantified decomposed image of the tissue with a standard reference model related to such tissue.
Broadly, the pathological tissue texture quantifier apparatus for use with a computing device comprises: a wavelet decomposer to decompose a region-of-interest of an ultrasound image of anatomical tissue into vertical, horizontal and diagonal detail coefficients; a wavelet energy calculator to calculate the energy content for each of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest of said ultrasound image of anatomical tissue; a wavelet energy summer to sum the energy contents of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI) of said ultrasound image of anatomical tissue; and, a wavelet normalizer to calculate a normalized energy content for each of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI) and a normalized energy content for said sum of said energy contents of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient.
While the wavelet decomposer also produces the approximation coefficients, I do not use the approximation coefficients in my invention.
Another object of the present invention is to provide a pathological tissue texture quantifier apparatus with a wavelet energy summer wherein said wavelet energy summer further sums the energy contents of said vertical detail coefficient and said horizontal detail coefficient of said region-of-interest (ROI) of said ultrasound image of anatomical tissue; and, wherein said wavelet normalizer further calculates a normalized energy content for said energy contents of said vertical detail coefficient and said horizontal detail coefficient.
It is a further object of the present invention to provide a pathological tissue texture quantifier apparatus which calculates said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) using the equation       E    z    =            (              1        /        N            )        *                  ∑                  i          =          1                N            ⁢              xe2x80x83            ⁢              "LeftBracketingBar"                  I          zi          2                "RightBracketingBar"            
wherein z=V, H, or D; i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and Izi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.
It a still further object of the present invention to provide a pathological tissue texture quantifier apparatus which normalizes said energy contents with an energy content of a non-decomposed image of said region-of-interest (ROI) defined by       E    O    =            (              1        /        N            )        *                  ∑                  i          =          1                N            ⁢              xe2x80x83            ⁢              "LeftBracketingBar"                  I          Oi          2                "RightBracketingBar"            
wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and Ioi=the pixel intensity value of the non-decomposed image of said region-of-interest (ROI).
It is still a further object of the present invention to provide a tissue pathology detection apparatus having a tissue pathology evaluator which comprises a standard reference model of energy content tissue references, for healthy and diseased or abnormal tissue, of vertical detail coefficients, horizontal detail coefficients and diagonal detail coefficients; energy content tissue references, for healthy and diseased or abnormal tissue, of normalized energy contents of each of said vertical, horizontal and diagonal detail coefficients; and, a comparer to compare said standard reference model with anatomical tissue.
It is a still further object of the present invention to provide a pathological tissue texture quantifier apparatus which uses the first level detail coefficients of a two-dimensional Haar wavelet decomposition transformation of said vertical, horizontal and diagonal detail coefficients.
It is a still further object of the present invention to provide a pathological tissue texture quantifier apparatus which is adapted to use a digitized xe2x80x9crawxe2x80x9d ultrasound image file of an ultrasound image of said anatomical tissue.
It is a still further object of the present invention to provide a pathological tissue texture quantifier apparatus which is adapted to use a digitized image signal from other non-invasive imaging techniques including, without limitation, magnetic resonance imaging (MRI), computer axial tomography (CAT Scan) three-dimensional ultrasound imaging, B-mode duplex scan, nuclear magnetic resonance, and radionuclide imaging. Preferably, such digitized image signal is a digitized image signal which has not been enhanced through digital xe2x80x9crawxe2x80x9d image processing or again converted to analog form (video) and then digitized again.
It is a still further object of the present invention to provide a pathological tissue texture quantifier apparatus which is adapted to quantify anatomical tissue such as muscle tissue and organ tissue of Homo sapiens and other mammals.
It is a still further object of the present invention to provide a pathological tissue texture quantifier apparatus which is adapted to detect early cardiomyopathy.
In view of the above, it is a still further object of the present invention to provide a method for quantifying pathological tissue texture comprising the steps of:(a) wavelet decomposing a region-of-interest of an ultrasound image of anatomical tissue into vertical, horizontal and diagonal detail coefficients; (b) calculating the energy content for each of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest of said ultrasound image of anatomical tissue; (c) summing the energy contents of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI) of said ultrasound image of anatomical tissue; (d) normalizing each of the energy contents for each of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI); and, (e) normalizing said sum of said energy contents of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient.
It is a still further object of the present invention to provide a method for quantifying pathological tissue texture wherein said step of (c) further comprises the step of: (c1) summing the energy contents of said vertical detail coefficient and said horizontal detail coefficient of said region-of-interest (ROI) of said ultrasound image of anatomical tissue.
It is a still further object of the present invention to provide a method for quantifying pathological tissue texture wherein said step of (e) further comprises the step of: (e1) normalizing the sum of the energy contents of said vertical detail coefficient and said horizontal detail coefficient.
It is a still further object of the present invention to, provide a method for quantifying pathological tissue texture further comprising the step of: (f) comparing said anatomical tissue with a standard reference model of energy content tissue. references.
Furthermore, an object of the present invention is to provide a method and apparatus which is adapted to process conventional echocardiographic image signals of animal (including human) tissue in order to detect minor changes in intracellular structure.
A still further object of the present invention is to provide a method and apparatus which processes the digitized xe2x80x9crawxe2x80x9d ultrasound image signal of a two-dimensional echocardiographic image, or, in other words, a rectangular coordinate array of pixels, which exists prior to the digital image processing which is typically necessary for video display of an echocardiographic image.
It is a still further object of the present invention to provide a method and apparatus which creates a plurality of first level detail coefficients using the Haar two-dimensional wavelet decomposition method without an image extension algorithm and/or distance function used therewith.
It is a still further object of the present invention to provide a method and apparatus which creates a plurality of first level detail coefficients using the two-dimensional wavelet decomposition method without an image extension algorithm and/or distance function used therewith.
It is a still further object of the present invention to provide a method and apparatus for detecting tissue pathology by other than histological or biochemical means while still using non-invasive imaging techniques.
It is a still further object of the invention to provide a method and apparatus for cataloging normal tissue and abnormal or diseased tissue for later use as a reference.
It is still a further object of the invention to provide a tool for enhancing patient care.
It is still a further object of the invention to provide a tool for enhancing the clinical study of various pathologies.
It is a still further object of the invention to provide a method and apparatus for cataloging normal tissue and abnormal or diseased tissue for later use as a reference.
Given that patients receive routine-frequent heart biopsies to screen for early heart transplant rejection, it is a further object of my invention to non-invasively detect early heart transplant rejection.
Given that patients receive routine-frequent pancreas biopsies to screen for early pancreas transplant rejection, it is a further object of my invention to non-invasively detect early pancreas transplant rejection.
Given that patients receive routine-frequent organ biopsies to screen for early organ transplant rejection, it is a further object of my invention to non-invasive detect early organ transplant rejection
Given that some drugs used to treat malignancies will cause the heart to dilate and subsequently cause heart failure, it is a still further object of my invention to provide for early detection of cardiomyopathy and, particularly, before such cardiomyopathy is irreversible.
The above and other objects of the present invention will become apparent from the drawings, the description given herein, and the appended claims.